Python - pedaços e fendas

Chunking é o processo de agrupar palavras semelhantes com base na natureza da palavra. No exemplo a seguir, definimos uma gramática pela qual o trecho deve ser gerado. A gramática sugere a sequência de frases como substantivos e adjetivos etc. que serão seguidos ao criar os pedaços. A saída pictórica dos blocos é mostrada abaixo.

import nltk
sentence = [("The", "DT"), ("small", "JJ"), ("red", "JJ"),("flower", "NN"), 
("flew", "VBD"), ("through", "IN"),  ("the", "DT"), ("window", "NN")]
grammar = "NP: {
      
       ?
       
* }" cp = nltk.RegexpParser(grammar) result = cp.parse(sentence) print(result) result.draw()

When we run the above program we get the following output −

chunk_1.PNG

Changing the grammar, we get a different output as shown below.

import nltk
sentence = [("The", "DT"), ("small", "JJ"), ("red", "JJ"),("flower", "NN"),
 ("flew", "VBD"), ("through", "IN"),  ("the", "DT"), ("window", "NN")]
grammar = "NP: {
      
? * }" chunkprofile = nltk.RegexpParser(grammar) result = chunkprofile.parse(sentence) print(result) result.draw()

When we run the above program we get the following output −

chunk_2.PNG

Chinking

Chinking is the process of removing a sequence of tokens from a chunk. If the sequence of tokens appears in the middle of the chunk, these tokens are removed, leaving two chunks where they were already present.

import nltk
sentence = [("The", "DT"), ("small", "JJ"), ("red", "JJ"),("flower", "NN"), ("flew", "VBD"), ("through", "IN"),  ("the", "DT"), ("window", "NN")]
grammar = r"""
  NP:
    {<.*>+}         # Chunk everything
    }
      
       +{      # Chink sequences of JJ and NN
  """
chunkprofile = nltk.RegexpParser(grammar)
result = chunkprofile.parse(sentence) 
print(result)
result.draw()

      

When we run the above program, we get the following output −

chink.PNG

As you can see the parts meeting the criteria in grammar are left out from the Noun phrases as separate chunks. This process of extracting text not in the required chunk is called chinking.